diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
index c4cf804126328b27fd56091d70f0b6e658b5b3c1..d167f2312ff240ed3bace1ef6bdd69d243cc399e 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
@@ -127,8 +127,12 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
ofm_dim_h = compute_conv_output_dim(ifm_dim_h, k_h, stride_h, pad, dilation_h)
ofm_dim_w = compute_conv_output_dim(ifm_dim_w, k_w, stride_w, pad, dilation_w)
assert ifm_ch % simd == 0, "SIMD must divide IFMChannels"
- wf = int((k_h * k_w * ifm_ch) // simd)
- folded_oshape = (1, ofm_dim_h, ofm_dim_w, wf, simd)
+ if self.use_parallel_window_output():
+ wf = int((ifm_ch) // simd)
+ folded_oshape = (1, ofm_dim_h, ofm_dim_w, wf, k_h * k_w * simd)
+ else:
+ wf = int((k_h * k_w * ifm_ch) // simd)
+ folded_oshape = (1, ofm_dim_h, ofm_dim_w, wf, simd)
return folded_oshape
def make_shape_compatible_op(self, model):
@@ -156,8 +160,6 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
return DataType[self.get_nodeattr("outputDataType")]
def get_instream_width(self):
- """Returns stream width, input and output stream width are equal for
- the sliding window function"""
ibits = self.get_input_datatype().bitwidth()
simd = self.get_nodeattr("SIMD")
ifm_ch = self.get_nodeattr("IFMChannels")
@@ -166,10 +168,13 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
return in_width
def get_outstream_width(self):
- """Returns stream width, input and output stream width are equal for
- the sliding window function, so the function to determine the input
- stream width can be reused."""
- return self.get_instream_width()
+ if self.use_parallel_window_output():
+ # feed all window pixels in parallel
+ k_h, k_w = self.get_nodeattr("ConvKernelDim")
+ return self.get_instream_width() * k_h * k_w
+ else:
+ # if parallel variant not in use: same width for output and input stream
+ return self.get_instream_width()
def get_number_output_values(self):
folded_oshape = self.get_folded_output_shape()
@@ -205,6 +210,22 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
return (ifm_ch, ifm_dim, ofm_dim, k, stride, dilation)
+ def use_parallel_window_output(self):
+ # Check if simple "ConvolutionInputGenerator_1D_parallel" variant can be used to
+ # feed window in parallel to the following layer, enabling full SIMD unfolding.
+ stride = self.get_nodeattr("Stride")
+ dilation = self.get_nodeattr("Dilation")
+ stride_h, stride_w = stride
+ dilation_h, dilation_w = dilation
+
+ if self.get_nodeattr("SIMD") == self.get_nodeattr("IFMChannels"):
+ if self.get_nodeattr("depthwise") == 0:
+ if stride_h == 1 and stride_w == 1:
+ if dilation_h == 1 and dilation_w == 1:
+ return True
+ else:
+ return False
+
def get_exp_cycles(self):
simd = self.get_nodeattr("SIMD")
(
@@ -224,12 +245,15 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
# since mmv != 1 is not supported yet, we set mmv for now to 1
mmv = 1
# see https://github.com/Xilinx/finn-hlslib/blob/master/slidingwindow.h
- cycles_write_block = (ofm_dim_w * k_w * k_h * (ifm_ch / simd)) / mmv
- cycles_read_block = stride_w * ifm_dim_w * (ifm_ch / simd)
- max_cycles = max(cycles_write_block, cycles_read_block)
- exp_cycles = (
- ifm_dim_w * k_h * dilation_h * (ifm_ch / simd) + ofm_dim_h * max_cycles
- )
+ if self.use_parallel_window_output():
+ exp_cycles = k_w + ofm_dim_w
+ else:
+ cycles_write_block = (ofm_dim_w * k_w * k_h * (ifm_ch / simd)) / mmv
+ cycles_read_block = stride_w * ifm_dim_w * (ifm_ch / simd)
+ max_cycles = max(cycles_write_block, cycles_read_block)
+ exp_cycles = (
+ ifm_dim_w * k_h * dilation_h * (ifm_ch / simd) + ofm_dim_h * max_cycles
+ )
return int(exp_cycles)
@@ -522,39 +546,49 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
"ultra": "ap_resource_uram()",
}
hls_ram_style = map_to_hls_ram_style[ram_style]
- hls_call = "ConvolutionInputGenerator"
- # check which ConvolutionInputGenerator is needed
- dilation_h, dilation_w = self.get_nodeattr("Dilation")
- hls_call += "_NonSquare"
- if dilation_h > 1 or dilation_w > 1:
- hls_call += "_Dilated"
- if self.get_nodeattr("depthwise") == 1:
- hls_call += "_dws"
- self.code_gen_dict["$DOCOMPUTE$"] = [
- """{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1, Input_precision1,
- IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y, SIMD1, Stride1_x, Stride1_y,
- Dilation1_x, Dilation1_y> (in0, out, numReps, {});""".format(
- hls_call, hls_ram_style
- )
- ]
- elif self.get_nodeattr("depthwise") == 1:
- hls_call += "_dws"
+ # check which ConvolutionInputGenerator is needed
+ if self.use_parallel_window_output():
+ hls_call = "ConvolutionInputGenerator_1D_parallel"
self.code_gen_dict["$DOCOMPUTE$"] = [
- """{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1, Input_precision1,
- IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y, SIMD1, Stride1_x, Stride1_y>
+ """{}<ConvKernelDim1_x, IFMChannels1, Input_precision1,
+ IFMDim1_x, OFMDim1_x, SIMD1, Stride1_x>
(in0, out, numReps, {});""".format(
hls_call, hls_ram_style
)
]
else:
- self.code_gen_dict["$DOCOMPUTE$"] = [
- """{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1, Input_precision1,
- IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y, SIMD1, Stride1_x, Stride1_y>
- (in0, out, numReps, {});""".format(
- hls_call, hls_ram_style
- )
- ]
+ hls_call = "ConvolutionInputGenerator_NonSquare"
+ dilation_h, dilation_w = self.get_nodeattr("Dilation")
+ if dilation_h > 1 or dilation_w > 1:
+ hls_call += "_Dilated"
+ if self.get_nodeattr("depthwise") == 1:
+ hls_call += "_dws"
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1,
+ Input_precision1, IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y,
+ SIMD1, Stride1_x, Stride1_y, Dilation1_x, Dilation1_y>
+ (in0, out, numReps, {});""".format(
+ hls_call, hls_ram_style
+ )
+ ]
+ elif self.get_nodeattr("depthwise") == 1:
+ hls_call += "_dws"
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1,
+ Input_precision1, IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y,
+ SIMD1, Stride1_x, Stride1_y> (in0, out, numReps, {});""".format(
+ hls_call, hls_ram_style
+ )
+ ]
+ else:
+ self.code_gen_dict["$DOCOMPUTE$"] = [
+ """{}<ConvKernelDim1_x, ConvKernelDim1_y, IFMChannels1,
+ Input_precision1, IFMDim1_x, IFMDim1_y, OFMDim1_x, OFMDim1_y,
+ SIMD1, Stride1_x, Stride1_y> (in0, out, numReps, {});""".format(
+ hls_call, hls_ram_style
+ )
+ ]
def dataoutstrm(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
@@ -587,12 +621,21 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
self.code_gen_dict["$SAVEASCNPY$"] = []
def blackboxfunction(self):
- self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
- """void {}(hls::stream<ap_uint<SIMD1*Input_precision1>> &in0,
- hls::stream<ap_uint<SIMD1*Input_precision1>> &out)""".format(
- self.onnx_node.name
- )
- ]
+ if self.use_parallel_window_output():
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ """void {}(hls::stream<ap_uint<SIMD1*Input_precision1>> &in0,
+ hls::stream<ap_uint<ConvKernelDim1_x*SIMD1*Input_precision1>>
+ &out)""".format(
+ self.onnx_node.name
+ )
+ ]
+ else:
+ self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
+ """void {}(hls::stream<ap_uint<SIMD1*Input_precision1>> &in0,
+ hls::stream<ap_uint<SIMD1*Input_precision1>> &out)""".format(
+ self.onnx_node.name
+ )
+ ]
def pragmas(self):
self.code_gen_dict["$PRAGMAS$"] = ["#pragma HLS INTERFACE axis port=in0"]